gravity chains: estimating bilateral trade flows when parts ......for example, rauch (1999), brun et...

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NBER WORKING PAPER SERIES GRAVITY CHAINS: ESTIMATING BILATERAL TRADE FLOWS WHEN PARTS AND COMPONENTS TRADE IS IMPORTANT Richard Baldwin Daria Taglioni Working Paper 16672 http://www.nber.org/papers/w16672 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 January 2011 We would like to thank participants at workshop at the WTO and Oxford. The views expressed in this paper are those of the authors and do not necessarily represent those of the European Central Bank or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2011 by Richard Baldwin and Daria Taglioni. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

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  • NBER WORKING PAPER SERIES

    GRAVITY CHAINS: ESTIMATING BILATERAL TRADE FLOWS WHEN PARTSAND COMPONENTS TRADE IS IMPORTANT

    Richard BaldwinDaria Taglioni

    Working Paper 16672http://www.nber.org/papers/w16672

    NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

    Cambridge, MA 02138January 2011

    We would like to thank participants at workshop at the WTO and Oxford. The views expressed inthis paper are those of the authors and do not necessarily represent those of the European Central Bankor the National Bureau of Economic Research.

    NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies officialNBER publications.

    © 2011 by Richard Baldwin and Daria Taglioni. All rights reserved. Short sections of text, not to exceedtwo paragraphs, may be quoted without explicit permission provided that full credit, including © notice,is given to the source.

  • Gravity Chains: Estimating Bilateral Trade Flows When Parts And Components Trade Is ImportantRichard Baldwin and Daria TaglioniNBER Working Paper No. 16672January 2011, Revised June 2011JEL No. F1,F15

    ABSTRACT

    Trade is measured on a gross sales basis while GDP is measured on a net sales basis, i.e. value added.The rapid internationalisation of production in the last two decades has meant that gross trade flowsare increasingly unrepresentative of the value added flows. This fact has important implications forthe estimation of the gravity equation. We present empirical evidence that the standard gravity equationperforms poorly by some measures when it is applied to bilateral flows where parts and components trade isimportant. We also provide a simple theoretical foundation for a modified gravity equation that issuited to explaining trade where international supply chains are important.

    Richard BaldwinGraduate Institute, GenevaCigale 21010 LausanneSWITZERLANDand CEPRand also [email protected]

    Daria TaglioniCentre for Trade and Economic IntegrationInstitut de hautes études internationales et du développementThe Graduate Institute of International and Development Studies Case postale 136 - CH - 1211 Genève 21 - [email protected]

  • 2

    1. INTRODUCTION

    Trade is measured on a gross sales basis while GDP is measured on a value added basis. For the

    first decades of the postwar period, this distinction was relatively unimportant. Trade in

    intermediates was always important, but it was quite proportional to trade in final goods. The

    rapid internationalisation of supply chains in the last two decades has changed this (Yi 2003).

    Indeed, such trade has in recent decades boomed between advanced nations and emerging

    economies as well as among emerging nations – especially in Asia, where the phenomenon is

    known as “Factory Asia”. There are, however, similar supply chains in Europe and between the

    US and Mexico (Kimura, Fukunari, Yuya Takahashi and Kazunobu Hayakawa 2007). As a

    result, gross trade flows are increasingly unrepresentative of the value-added flows. This fact has

    important policy implications (Lamy 2010), but it also has important implications for one of

    trade economists‟ standard tools – the gravity equation.

    The basic point is simple. The standard gravity equation is derived from a consumer expenditure

    equation with the relative price eliminated using a general equilibrium constraint (Anderson

    1979, Bergstrand 1985, 1989, 1990). The corresponding econometrics widely used today is

    based on this theory (Anderson and Van Wincoop 2003). As such the standard formulation –

    bilateral trade regressed on the two GDPs, bilateral distance and other controls – is best adapted

    to explaining trade in consumer goods. When consumer trade dominates, the GDP of the

    destination nation is a good proxy for the demand shifter in the consumer expenditure equation;

    the GDP of the origin nation is a good proxy of its total supply. By contrast, when international

    trade in intermediate goods dominates, the use of GDPs for the supply and demand proxies is

    less appropriate.

    Consider, for instance, the determinants of Thai imports of auto parts from the Philippines. The

    standard formulation would use Thai GDP to explain Thailand‟s import demand, however, the

    underlying demand for parts is generated by Thai gross production of autos, not its value-added

    in autos. As long as the ratio of local to imported content does not change, value added is a

    reasonable proxy for gross output, so the standard regression is likely to give reasonable results.

    However, for regions where production networks are emerging, value added can be expected to

    be a poor proxy.

    Why do incorrectly specified mass variables matter? A large number of gravity studies focus on

    variables that vary across country pairs – say free trade agreements, cultural ties, or immigrant

    networks. The most recent of these studies employ estimators that control for the mass variables

    with fixed effects. Such studies do not suffer from mass-variable mis-specification and so are

    unaffected by our critique. There are however a number of recent studies – especially concerning

    the „distance puzzle‟ that do proxy for the production and demand variables with GDP. It is these

    studies that our work speaks to.

    For example, Rauch (1999), Brun et al (2005), Berthelon and Freund (2008), and Jacks et al

    (2008) use GDP as the mass variable when they decompose the change in the trade flow into the

    effects of income changes and trade cost changes; Anderson and Van Wincoop (2003) also use

    GDP as the mass variable in one of their estimation techniques. Since most of these studies are

    concerned with a broad set of nations and commodities, the mis-specification of the mass

    variable probably has a minor impact on the results – as the findings of Bergstrand and Egger

    (2010) showed. More worrying, however, is the use by authors that focus on trade in parts and

  • 3

    components such as Athukorala and Yamashita (2006), Kimura et al (2007), Yokota and

    Kazuhiko (2008), and Ando and Kimura (2009). These papers all use the consumer good version

    of the gravity model to describe parts and components trade and thus have mis-specified the

    mass variable.

    Literature review

    There is nothing new about trade in intermediates. Intermediates have long been important in the

    trade between the US and Canada; the 1965 US-Canada Auto Pact, for example, explicitly

    targeted preferential tariff reductions on cars and cars parts. It has also long been important

    within Western Europe as early studies of the EEC demonstrated (e.g. Dreze 1961, Verdoorn

    1960, and Balassa 1965, 1966). The famous book by Grubel and Lloyd (1975), made clear that

    much of intra-industry trade was in intermediates, not final goods, and the importance of

    intermediates was reflected in early work by well-known theorists. For example, Vaneck (1963)

    presents an extension of the Heckscher-Ohlin model that allows for intermediates trade, and

    Ethier (1981) casts his model of intra-industry trade in a world where all trade was in

    intermediates.

    As better data and computing technology became available, the importance of intermediates in

    trade was rediscovered and documented more thoroughly. In the context of efforts to understand

    the impact of the EU‟s Single Market Programme, European scholars focused on the role of

    intermediates. For example, Greenaway and Milner (1987) list this as one of the „unresolved

    issues‟, writing “it is becoming increasingly obvious that a significant proportion of measured

    IIT is accounted for by trade in parts and components. [Nevertheless,] most of the models

    developed so far assume trade in final goods. The modelling of trade in intermediates needs to be

    explored further." The issue attracted renewed interest following development of the new trade

    theory in the 1980s (Helpman and Krugman 1985)1 and again in the 1990s with Jones and

    Kierzkowski (1990), and Hummels, Rapoport and Yi (1998)2, and more recently Kimura,

    Takahashi and Hayakawa (2007), and Grossman and Rossi-Hansberg (2008).

    The traditional gravity model was developed in the 1960s to explain factory-to-consumer trade

    (Tinbergen 1962, Poyhonen 1963, Linnemann 1966). This concept is at the heart of the first clear

    microfoundations of the gravity equation – the seminal Anderson (1979).3 This article proposed a

    theoretical explanation of the gravity equation based on CES preferences when nations make a

    single differentiated product. Anderson and Van Wincoop (2003) use the Anderson (1979)

    theory to develop appropriate econometric techniques. Subsequent theoretical refinements have

    focused on showing that the gravity equation can be derived from many different theoretical

    1 As illustrated by the Brookings Institution book “The global factory: Foreign assembly in

    international trade” (Grunwald and Flam 1985).

    2 Feenstra (1998) for a survey of the 1990s literature.

    3 Leamer and Stern (1970) informally discusses three economic mechanism that might generate

    the gravity equations but these were based on rather exotic economic logics; Anderson (1979)

    was the first to provide clear microfoundations that rely only on assumptions that would strike

    present-day readers as absolutely standard.

  • 4

    frameworks (including monopolistic competition, and Melitz-type trade models with

    heterogeneous firms).4

    Studies on the gravity equations applicability to intermediate goods trade are more limited. These

    include Egger and Egger (2004), and Baldone et al (2007). The study that is closest to ours is

    Bergstrand and Egger (2010). These authors develop a computable general equilibrium model

    that explains the bilateral flows of final goods, intermediate goods and FDI. Calibration and

    simulation of the model suggests a theoretical rationale for estimating a near-standard gravity

    model for the three types of bilateral flows. Using a large dataset on bilateral flows of final and

    intermediate goods trade, and a dataset on bilateral FDI flows, they estimate the three equations

    and find that the standard gravity variables all have the expected size and magnitude.

    The value added of our paper is primarily empirical – to show that the standard gravity

    specification performs poorly when applied to flows where trade in intermediates is important.

    Moreover, the failures line up with the predictions of our simple theory model that suggests a

    gravity equation formulation that is appropriate to intermediates trade. Note that when we

    perform the estimates on data pooled across a wide range of nations – as do Bergstrand and

    Egger (2010) – we find the same results, namely that the standard specification performs well.

    We believe the difference in the results is due to the fact that for many trade flows, the pattern of

    trade in intermediates is quite proportional to trade in final goods. This is especially for trade

    among developed nations.

    Plan of the paper

    The paper starts with simple theory that generates a number of testable hypotheses. We then

    confront these hypotheses with the data and find that the estimated coefficients deviate from

    standard results in the way that the simple theory says they should. The key results are that the

    standard economic mass variable, which reflects consumer demand, does not perform well when

    it comes to bilateral trade flows where intermediates are dominant. Finally, we consider new

    proxies for the economic mass variables and show that using the wrong mass variable may bias

    estimates of other coefficients.

    2. THEORY

    To introduce notation and fix ideas, we review the standard gravity derivation following Baldwin

    and Taglioni (2007).5 Using the well-known CES preference structure for differentiated varieties,

    spending in nation-d („d‟ for destination) on a variety produced in nation-o („o‟ for origin) is:

    4 On the monopolistic competition frameworks see Krugman (1980), Bergstrand (1985, 1989),

    Helpman and Krugman, (1985); on the Heckscher-Ohlin model see Deardorff (1998), on

    Ricardian models see Eaton and Kortum (2001); on Melitz (2003) model applications, see

    Chaney (2008), and Helpman, Melitz and Rubinstein (2008).

    5 Another well-known derivation is from Helpman and Krugman (1985); they start from (1) and

    make supply-side assumptions that turns po into a constant, but makes nod proportional to nation-

    o‟s GDP so the resulting gravity equation is similar – at least in the case of frictionless trade (the

    case they worked with in 1985).

  • 5

    1;

    1

    d

    d

    od

    od EP

    pv (1)

    where odv is the expenditure in destination country-d, pod is the consumer price inside nation-d

    of a variety made in nation-o, dP is the nation-d CES price index of all varieties, is the

    elasticity of substitution among varieties ( > 1 is assumed throughout), and dE is the nation-d

    consumer expenditure.

    From the well-known profit maximization exercise of producers based in nation-o,

    odoodod mp , where od is the optimal price mark-up, om is the marginal costs, and od is

    the bilateral trade cost factor, i.e. 1 plus the ad valorem tariff equivalent of all natural and

    manmade barriers. The mark-up is identical for all destinations if we assume perfect competition

    or Dixit-Stiglitz monopolistic competition; in these cases, the price variation is characterised by

    “mill pricing”, i.e. 100% pass through of trade costs to consumers in the destination market.6

    Here we work with Dixit-Stiglitz competition exclusively, so the mark-up is always /(-1).

    This means the local consumer price is ooooo mp ))1/(( , where oo is unity as we assume

    away internal trade barriers. Using this and summing over all varieties (assuming symmetry of

    varieties by origin nation for convenience), we have:

    dd

    od

    ooood EP

    pnV

    1

    11

    (2)

    where odV is the aggregate value of the bilateral flow (measured in terms of the numeraire) from

    nation-o to nation-d; on is the number (mass) of nation-o varieties (all of which are sold in

    nation-d as per the well-known results of the Dixit-Stiglitz-Krugman model).

    To turn this expenditure function (with optimal prices) into a gravity equation, we impose the

    market-clearing condition. Supply and demand match when (2) – summed across all destinations

    (including nation-o‟s sales to itself) – equals nation-o‟s output. When there is no international

    sourcing of parts, the nation‟s output is its GDP, denoted here as Yo. Thus the market-clearing

    condition is: d ddodoooo EPpnY111 . Solving this we obtain that ooooo Ypn

    /1 where o

    is the usual market-potential index (namely, the sum of partners‟ market sizes weighted by a

    distance-related weight that places lower weight on more remote destinations); specifically it is

    d ddodo EP11 . Plugging this into (2) yields the traditional gravity equation:

    od

    odododP

    YEV

    111

    1

    (3)

    Here Pd is the nation-d CES price index, while o is the nation-o market-potential index. It has

    become common to label the product odP 1

    as the “multilateral trade resistance” term.

    6 If one works with the Ottaviano Tabuchi and Thisse (2002) monopolistic competition

    framework, the mark-up varies bilaterally and so mill-pricing is not optimal.

  • 6

    However, it is insightful to keep in mind the fact that “multilateral trade resistance” is a

    combination of two well-known, well-understood, and frequently measured components.

    In the typical gravity estimation, Ed is proxied with nation-d‟s GDP, Yd is proxied with nation-

    o‟s GDP, and is proxied with bilateral distance.

    2.1. Gravity when parts and components trade is important

    To extend the gravity equation to allow for parts and components trade among firms, we need a

    trade model where intermediate goods trade is explicitly addressed. It proves convenient to work

    with the Krugman and Venables (1996) “vertical linkages” model which focuses squarely on the

    role of intermediate goods. Here we present the basic assumptions and the manipulations that

    produce the modified gravity equation.

    Krugman and Venables (1996) works with the standard new economic geography model where

    each nation has two sectors (a Walrasian sector, A, and a Dixit-Stiglitz monopolistic competition

    sector M), and a single primary factor, labour L. Production of A requires only L, but production

    of each variety of X requires L and a CES composite of all varieties as intermediate inputs (i.e.

    each variety is purchased both for final consumption and for use as an intermediate). Following

    Krugman and Venables (1996), the CES aggregate on the supply side is isomorphic to the

    standard CES consumption aggregate.

    The indirect utility function for the typical consumer is:

    )1/(111 ;;/

    Gi iAcc dipPPpPPIV (4)

    where I is consumer income, Pc is the ideal consumer price index, pA is the price of A, the

    parameter “” is the Cobb-Douglas expenditure share for M-sector goods, is the elasticity of

    substitution among varieties, P is the CES price index for M varieties, pi is the consumer price of

    variety i, and G is the set of varieties available.

    The cost function of a typical firm in a typical country is:

    PwxaFxPwC X 1],,[ (5)

    Here x is the output of a typical variety, F and aX are cost parameters, w is the wage, and is the

    Cobb-Douglas cost share for intermediate inputs.7

    As noted above, mill pricing is optimal under Dixit-Stiglitz monopolistic competition. This,

    combined with the identity of the elasticity of substitution, , for each good‟s use in

    consumption and production, tells us that the price of each variety will be identical across the

    two types of customers. Choosing units such that aX = 1-1/, the landed price will be:

    doPwp ooodod ,;1

    (6)

    7 The assumption that the Cobb-Douglas parameter is identical in the consumer and producer

    CES price index is one of the strategic implications in the Krugman-Venables model; see their

    book for a careful examination of what happens when this is relaxed (Fujitu, Krugman and

    Venables 1999). The standard conclusion is that it does not qualitatively change results but it

    does significantly complicate the analysis in a way that requires numerical simulation.

  • 7

    Using Shepard‟s and Hotelling‟s lemmas on (4) and (5), and adding the total demand for

    purchasers located in nation-d, we have an expression that is isomorphic to (2) except the

    definition of E now includes purchases by customers using the goods as intermediates:

    )(;1

    1

    1

    ddddd

    d

    od

    ooood CnIEEP

    pnV

    (7)

    where Id is nation-d‟s consumer income and Cd is the total cost of a typical nation-d variety.

    As before, we solve for the endogenous nopoo1-

    using the market-clearing condition. In this case,

    the value that nation-o must sell is the full value of its M-sector output (not just its value added).

    Under monopolistic competition‟s free entry assumption, the value of sales equals the value of

    full costs, so the market clearing equation becomes:

    ],,[;111

    ooooddoddooooo xPwCCEPpnCn

    (8)

    where the cost function C is given in (5). Solving (8) and plugging the result into (7) yields a

    gravity equation modified to allow for intermediates goods trade, namely:

    od

    odododP

    CEV

    111

    1

    (9)

    where Ed is defined in (7) and Co is defined in (8), and d ddodo EP11 .

    Expression (9) is the gravity equation modified to allow for trade intermediates. The key

    differences show up in the definition of the economic “mass” variables since purchases

    are now driven both by consumer demand (for which income is the demand shifter) and

    intermediate demand (for which total production costs is the demand shifter).

    3. BREAKDOWN OF THE STANDARD GRAVITY MODEL

    This theory exercise suggests a key difference that should arise between gravity estimates on

    nations and time periods where most imports are consumer goods versus those where

    intermediates trade is important. Specifically, the standard practice of using the GDP of origin

    and destination countries as the „mass‟ variables in the gravity equations is inappropriate for

    bilateral flows where parts and components are important. Of course, if the consumer- and

    producer-demand moves in synch – as they may in a steady-state situation – then GDP may be a

    reasonable proxy for both consumer and producer demand shifter. But if the role of vertical

    specialisation trade is changing over time, GDP should be less good at proxy-ing for the

    underlying demand shifters. For this reason, we expect that origin-country‟s GDP and destination

    country‟s GDP will have diminished explanatory power for those countries where value-chain

    trade is important.

    These observations generate a number of testable hypotheses.

    The estimated coefficient on the GDPs should be lower for nations where parts trade is important, and should fall as the importance of parts trade rises.

    As vertical specialisation trade has become more important over time, the GDP point estimates should be lower for more recent years.

  • 8

    In those cases where the GDPs of the trade partners lose explanatory power, bilateral trade should be increasingly well explained by demand in third countries.

    For example, China‟s imports should shift from being explained by China‟s GDP to being

    explained by its exports to, say, the US and the EU. There are two ways of phrasing this

    hypothesis. First, China‟s imports are a function of its exports rather than its own GDP. Second,

    China‟s imports are a function of US and EU GDP rather than its own, since US and EU GDP

    are critical determinants of their imports from China.

    To check these conjectures, we estimate the standard gravity model for different sets of countries

    and sectors for a panel that spans the years 1967 to 2007. We run standard log-linear gravity

    equations using pooled cross-section time series data, namely:

    odtodtdt

    dt

    ot

    otodt

    P

    EYGV

    lnln)ln( 211 (10)

    A key econometric problem is that the price index Pdt and the market potential index ot are

    unobservable and yet include factors that enter the regressions independently (e.g. E, Y and ).

    Thus ignoring them can lead to serious biases.

    If the econometrician is only interested in estimating the impact of a pair-specific variable – such

    as distance or tariffs – the standard solution is to put in time-varying country-specific fixed

    effects. This eliminates all the terms multiplied by 1 in equation (10). Plainly we cannot use this

    approach to investigate the impact of using GDPs as the economic mass proxies when trade in

    parts and components is important. We thus need other means of controlling for to

    and dtP .

    Our baseline specification accounts for the terms to

    and dtP explicitly. As precise measures of

    to and dtP

    are hard to construct, we perform robustness checks using fixed effects

    specifications. To ensure comparability with the fixed effects specification, in the key

    specifications we enter the importer‟s and exporter‟s economic mass as a single product-term

    into the equation, with the shortcoming of forcing the coefficient of the importer and exporter

    mass variables to be the same. Specifically, the term accounting for the product of the trade

    partners‟ economic mass is the product of importer-d real GDP (so to account for dtP ) and of

    exporter-o‟s nominal GDP divided by a proxied for to

    , constructed adapting a method first

    introduced by Baier and Bergstrand (2001) namely:

    11

    1)(*d oddtot

    DistGDP

    The elasticity value in the to

    relationship has been set as σ = 4, which corresponds to estimates

    proposed in empirical literature (e.g. Obstfeld and Rogoff, 2001 and Carrere 2006).

    Turning to the trade cost variable, , we introduce standard trade frictions, including log of

    bilateral distance, and dummies for contiguity, and common language. Moreover for robustness

    purposes we also test for additional time-varying trade frictions measured by cif-fob ratios, as

    proposed by Bergstrand and Egger (2010).

    The data used for the bilateral trade flows, and the cif-fob ratios are taken from the UN

    COMTRADE database. GDPs are from the World Bank‟s World Development Indicators.

  • 9

    Bilateral distances, contiguity, and common language are from the CEPII database. Data for

    Taiwan, which are missing from the UN databases, are from CHELEM (CEPII) and national

    accounts.

    Estimation is by simple ordinary least squares with the standard errors clustered by bilateral pairs

    since we work in direction-specific trade flows rather than the more traditional average of

    bilateral flows.

    3.1. Empirical results

    In Table 1 we report the gravity equation estimates for all goods as well as for intermediate and

    final goods separately. Intermediate and final goods have been identified according to the UN

    Broad Economic Categories Classification (see appendix). The sample includes all the nations

    where data is available, namely 187 nations.

    Coefficients have the expected signs and are statistically significant. For all six regressions (all

    goods, only intermediates, and only consumer goods with and without time fixed effects) the

    estimates are broadly similar. The mass variables are all estimated to be close to unity. The

    bilateral distance variable is negative and falls in the expected range. The additional trade cost

    measure, the cif/fob ratio, is always negative as expected for the sub-samples, but positive for the

    aggregate sample. Continuity and language always have the expected sign and fall in the usual

    ranges.

    Table 1: Bilateral flows of total, intermediate and final goods, 187 nations, 2000-2007.

    All goods Intermediates only Consumer goods only

    VARIABLES (1) (2) (3) (4) (5) (6)

    ln (GDPot*GDPdt/Ωot*Pdt) 0.860*** 0.865*** 0.898*** 0.905*** 0.791*** 0.796***

    (0.006) (0.006) (0.007) (0.007) (0.008) (0.008)

    ln(cif/fob ratio) -

    0.0833*** -

    0.0798*** -0.189*** -0.184*** -0.341*** -0.338***

    (0.013) (0.013) (0.015) (0.015) (0.017) (0.017)

    ln Distance -0.775*** -0.777*** -0.851*** -0.855*** -0.758*** -0.760***

    (0.019) (0.019) (0.022) (0.022) (0.025) (0.025)

    Contiguity 1.575*** 1.565*** 1.711*** 1.697*** 1.356*** 1.347***

    (0.105) (0.105) (0.119) (0.119) (0.127) (0.127)

    Common language 0.966*** 0.972*** 0.997*** 1.005*** 1.186*** 1.192***

    (0.046) (0.046) (0.052) (0.052) (0.059) (0.059)

    Constant -28.61*** -28.74*** -30.84*** -31.03*** -26.87*** -27.02***

    (0.359) (0.363) (0.400) (0.404) (0.456) (0.459)

    Time dummies

    yes

    yes

    yes

    Observations 62875 62875 62875 62875 58468 58468

    R-squared 0.627 0.628 0.585 0.587 0.479 0.480

    Source: Authors‟ calculations; Note: Dependent variable: imports + re-imports. Standard errors are clustered by bilateral pair. Robust

    standard errors are reported in parenthesis: *** p

  • 10

    These Table 1 results confirm the findings of Bergstrand and Egger (2010), namely that the size

    of the estimated coefficients does not vary for consumer and intermediate goods. As such, it

    would seem that our concern about mis-estimating the gravity equation is misplaced. However,

    as noted above, if the consumer and intermediate trade is roughly proportional over time, GDP

    will be a reasonable proxy for both consumer income and gross value added. The real test of the

    stability of the parameters would be on a sample where the importance of intermediates trade

    was rising significantly.

    Table 2: Bilateral flows of total goods among Factory Asia nations (1967-2008).

    No time interactions Variable mass coefficient

    VARIABLES (1) (2) (3) (4) (5) ln (GDPoGDPd/ΩoPd) 0.725*** 0.725*** 0.764*** 0.425*** 0.504***

    (0.009) (0.028) (0.026) (0.055) (0.051)

    *years 1967-1986

    0.318*** 0.278***

    (0.048) (0.048)

    *years 1987-1996

    0.177*** 0.164***

    (0.027) (0.032)

    *years 1998-2002

    0.007 0.00274

    (0.015) (0.017)

    ln (Distance) -0.258*** -0.258

    -0.0414

    (0.0570) (0.298)

    (0.297)

    Contiguity 0.188*** 0.188

    0.167

    (0.0682) (0.386)

    (0.367)

    Colony -0.487*** -0.487

    0.0695

    (0.101) (0.388)

    (0.405)

    Common coloniser -0.620*** -0.620*

    -0.296

    (0.116) (0.325)

    (0.324)

    Constant -7.218*** -7.218*** -8.825*** -1.465 -2.632**

    (0.433) (2.281) (0.485) (2.279) (1.178)

    Time effects yes yes

    Exporter*time effects

    yes yes yes

    Importer*time effects

    yes yes yes Pair effects

    yes yes yes

    Clustered Standard Errors yes yes yes yes Observations 1722 1722 1722 1722 1722 R-squared 0.833 0.833 0.936 0.851 0.948

    Source: Authors‟ calculations; Note: Standard errors are clustered by bilateral pair. Robust standard errors in parenthesis: *** p

  • 11

    coefficients which are 0.3 for the pre-Factory Asia period (Baldwin 2006), 0.2 for the 1987-1996

    period, and essentially zero (and insignificant) for the post 1998 period.

    Figure 1: GDP coefficients for Factory Asia countries, 1967-2008.

    19851995

    0

    0.2

    0.4

    0.6

    0.8

    1

    1.2

    1967

    1968

    1969

    1970

    19

    71

    1972

    1973

    1974

    1975

    1976

    19

    77

    1978

    1979

    1980

    1981

    1982

    19

    83

    1984

    1985

    1986

    1987

    1988

    19

    89

    1990

    1991

    1992

    1993

    1994

    19

    95

    1996

    1997

    1998

    1999

    2000

    20

    01

    2002

    2003

    2004

    2005

    2006

    20

    07

    Year Coeficient

    Note

    s: Estimated mass-elasticity coefficients with year interactions and pair fixed effects (as in (10). High and low bars show plus/minus 2

    standard errors; Factory Asia countries: Japan, Indonesia, Republic of Korea, Malaysia, Thailand, and Taiwan.

    To estimate the mass variable‟s instability over time more clearly, we re-do the same regression

    but allowing yearly interaction terms. The results, displayed in Figure 1, shows the evolution of

    the GDP coefficients. The mass elasticity fall over time, with two clear breaks in the estimated

    coefficients, 1985 and 1998.

    The timing and direction of these structural changes are very much in line with the literature on

    the internationalisation of production. According to many studies, production unbundling started

    in the mid-1980s and accelerated in the 1990s (e.g. Hummels, Rapport and Yi 1998). The idea is

    that coordination costs fell with the ICT revolution and this permitted the spatial bundling of

    production stages (Baldwin 2006). The ICT revolution came in two phases. The internet came

    online in a massive way in the mid-1980s, and then, in the 1990s, the price of

    telecommunications plummeted with various ITC-related technical innovations and widespread

    deregulation (Baldwin 2011). The upshot of all these changes was that it became increasingly

    economical to geographically separate manufacturing stages. Stages of production that

    previously were performed within walking distance to facilitate face-to-face coordination could

    be dispersed without an enormous drop in efficiency or timeliness.

    As far as the Figure 1 results are concerned, the notion is that as trade became increasingly

    focused on intermediates, GDP became an increasingly poor determinant of trade flows – as

  • 12

    suggested by our theory. The impact of the mid-1980s changes and the mid-1990s changes are

    clear from the estimated GDP elasticities. More specifically, from 1967 to 1985 the elasticity of

    these countries‟ bilateral imports to GDP was stable, with a coefficient of about 0.77. Between

    1985 and 1997, it steadily decreased to reach a coefficient value of about 0.60, and after 1998, it

    further dropped to a figure close to 0.40. The coefficient estimates for the different periods in

    Factory Asia are summarised in Table 2 , columns (4) and (5).

    Table 3: Estimates for EU15, and US, Canada, Australia and New Zealand, 1967-2008.

    VARIABLES No time interactions Variable mass coefficient

    (1) (2) (3) (4) (5)

    ln(GDPoGDPd/ΩoPd) 0.659*** 0.659*** 0.632*** 0.725*** 0.703***

    (0.009) (0.025) (0.027) (0.058) (0.034)

    *years 1967-1986

    -0.0408 -0.0503

    (0.051) (0.044)

    *years 1987-1996

    -0.0376 -0.0444

    (0.036) (0.032)

    *years 1998-2002

    0.0132 0.005

    (0.017) (0.014)

    ln (Distance) -0.843*** -0.843***

    -0.688**

    (0.059) (0.233)

    (0.276)

    Constant -1.630** -1.630 -8.819*** -4.966 -10.72***

    (0.726) (2.284) (0.657) (3.733) (0.917)

    Time effects yes yes

    Exporter*time effects

    yes yes yes

    Importer*time effects

    yes yes yes

    Pair effects

    yes yes yes

    Observations 820 820 820 820 820

    R-squared 0.932 0.932 0.978 0.934 0.978

    Clustered Standard Errors yes yes yes yes

    Source: Authors‟ calculations; Note: Standard errors are clustered by bilateral pair. Robust standard errors are reported in parenthesis:

    *** p

  • 13

    3.2. More precise estimates of the impact of components on the mass estimate

    These two sets of results are highly suggestive. On data that is widely recognised as being

    dominated by parts and components trade, we find structural instability in the mass variable

    coefficient moving in the expected direction. However, on data where this sort of production

    fragmentation is not widely viewed as having been important, we find that that mass point-

    estimates are stable over time.

    To explore this more systematically, we consider a more continuous relationship between the

    importance of components trade and the point-estimate on the mass variable on the full sample.

    Our basic assertion is that the composition of trade flows will influence the point estimates of the

    economic mass variables since the standard gravity model is mis-specified when it comes to the

    mass variable. The most direct test of this hypothesis is to include the ratio of intermediates to

    total trade as a regressor, both on its own and – more importantly – as an interaction term with

    the economic mass variable. Of course a mis-specification of one part of the regression has

    implications for the point-estimates of the other regressors, so we also consider the ratio‟s

    interaction with the other main regressors.

    To this end, we re-estimate the basic equation on the full sample of 187 countries for the years

    2000-2008 allowing for interactions with a variable that accounts for the share of intermediate

    goods over total imports in each particular bilateral trade flow.

    The idea here is that GDP as a measure for economic mass should work less well for those

    bilateral flows that are marked by relatively high shares of intermediates trade. By estimating the

    effect on the full sample, we avoid the problem of identifying the exact sources of the variation

    in the coefficients. We implement the idea in two ways.

    First we estimate the standard regression but include the share of bilateral imports that is in

    intermediates (denoted as Mdinterm

    /Md). This new variable is included on its own and interacted

    with the other right-hand side variables. Table 4 reports the estimated results for the coefficients

    of interest.

    The regression results tend to confirm our hypothesis. The regression reported in column (1)

    includes the ratio on its own and interacted only with the mass variable. The coefficients for

    economic mass and distance are a very reasonable at 1.031 and -1.173 respectively (both

    significant at the 1% level). The ratio on its own comes in positive as expected (bilateral trade-

    links marked by a high share of intermediates tend to have „too much‟ trade compared to the

    prediction of the standard gravity equation). The ratio interacted with economic mass also has a

    negative sign, -0.129, which conforms with our hypothesis (the higher is the ratio of

    intermediates for the particular trade pair, the lower is the estimate of the economic mass

    variable). All coefficients are significantly different to zero at the 1% level of confidence.

    The other columns report robustness checks on the main regression. The qualitative results on

    the variables of interest (the mass coefficient, the ratio coefficient, and the mass*ratio interaction

    coefficient) are robust to inclusion of interaction terms with any or all of the control variables.

    This confirms the more informal tests based on an a priori separation of the sample.

    Interestingly, the interaction term is also highly significant and negative for distance in

    specification (2). That is, distance seems to matter more for components trade – a result that is

    not in line with our simple model, but is expected from the broader literature on offshoring. For

  • 14

    example, transportation costs become more important when trade costs are incurred between

    each stage of production while the value added per stage is modest.

    Table 4: Interactions with share of intermediates in total imports, full sample.

    VARIABLES (1) (2) (3) (4)

    Mdinterm

    /Md 6.536*** 8.018*** 6.954*** 7.330***

    (0.858) (1.015) (0.835) (1.004)

    ln (GDPoGDPd/ΩoPd) 1.031*** 1.027*** 1.064*** 1.058***

    (0.010) (0.010) (0.010) (0.010)

    * Mdinterm

    /Md -0.129*** -0.118*** -0.137*** -0.126***

    (0.017) (0.017) (0.017) (0.016)

    ln (Distance) -1.173*** -1.051*** -1.011*** -0.954***

    (0.018) (0.037) (0.0191 (0.037)

    * Mdinterm

    /Md

    -0.232***

    -0.110*

    (0.059)

    (0.0601

    Contigod

    1.350*** 0.967***

    (0.101) (0.246)

    * Mdinterm

    /Md

    0.625*

    (0.369)

    Common language

    1.215*** 1.126***

    (0.044) (0.078)

    * Mdinterm

    /Md

    0.178

    (0.119)

    Constant -27.58*** -28.40*** -30.85*** -31.07***

    (0.551) (0.634) (0.541) (0.625)

    Observations 121737 121737 121737 121737

    R-squared 0.604 0.604 0.621 0.621

    Notes: Mdtinterm

    /Md is the share of intermediate imports by a country d over its total imports. Robust standard errors are reported in

    parenthesis: *** p

  • 15

    the 1% level. The additional effects lower the base case point-estimate by around 0.10. The

    distance term is a very reasonable -1.1 and highly significant.

    Table 5: All countries, 2000-2007, by share of intermediate imports.

    Variables (GDPoGDPd/ΩoPd) ln(Distance) Constant

    Base effect 0.985*** -1.105*** -26.29***

    (0.018) (0.018) (0.898)

    Base effect * d2 -0.0308

    (0.021)

    Base effect * d3 0.0108

    (0.021)

    Base effect * d4 -0.0330

    (0.020)

    Base effect * d5 -0.0803***

    (0.020)

    Base effect * d6 -0.103***

    (0.021)

    Base effect * d7 -0.0903***

    (0.021)

    Base effect * d8 -0.0723***

    (0.022)

    Base effect * d9 -0.118***

    (0.024)

    Base effect * d10 -0.0748*** (0.022)

    Observations 121712 R-squared 0.610

    Source: Authors‟ estimations; Note: deciles categorise countries bilateral imports by increasing shares of intermediate imports over total

    imports. Hence q10 indicates the 10% bilateral import relationships where the share of intermediate imports in total imports is highest

    and the base effect the 10% bilateral import relationships where the share of intermediate imports in total imports is lowest. Common

    language and contiguity included by not reported. Standard errors are clustered by bilateral pair. Robust standard errors are reported in

    parenthesis: *** p

  • 16

    0.8

    0.9

    0.9

    1.0

    1.0

    1.1

    0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    siz

    e c

    oe

    ffic

    ien

    t

    share of intermediates in total imports

    Source: Authors‟ estimations; Note: horizontal bars represent estimated coefficient and vertical bars twice the standard errors.

    4. A SEARCH FOR MASS PROXIES WHEN INTERMEDIATES ARE IMPORTANT

    The previous section provides clear evidence that the standard gravity equation is “broken” when

    it comes to bilateral flows where intermediates trade is important. The theory suggests that the

    perfect solution would require data on total costs to construct the demand shifter for

    intermediates imports. If the economy is reasonably competitive, gross sales would be a good

    proxy for the total costs. Unfortunately, such data are not available for a wide range of nations

    especially the developing nations where production fragmentation is so important. On the mass

    variable for the origin nation, theory suggests that we use gross output rather than value added.

    Again such data are not widely available.

    This section presents the results of our search for a pragmatic “repair” which relies only on data

    that is available for a wide range of nations. The basic thrust is to use the theory in Section 2 to

    develop some proxies for economic mass variables that better reflect the fact that the demand for

    intermediates depends upon gross output, not value added.

    4.1. Fixes for economic mass proxies

    We start with the destination nation‟s mass variable. In Section 2 we showed that a bilateral flow

    of total goods is the sum of goods whose demand depends upon the importing nation‟s GDP (i.e.

    consumer goods) and goods whose demand depends upon the total costs of the sector buying the

    relevant intermediates. The theory says that our economic mass measure should be a linear

    combination of two mass measures, not a log-linear combination (see expressions (9) and (7)).

    This suggests a first measure that adds imports of intermediates to GDP. The idea here is to

    exploit the direct definition of total costs as the cost of primary inputs plus the value of

    intermediate inputs. For any given local firm, some of the intermediates it purchases will be from

    local suppliers, but summing across all sectors and firms within a single nation, such

    intermediates will cancel out leaving only payments to local factors of production and imports of

  • 17

    intermediates. Our first pragmatic fix therefore is to measure the destination nation‟s demand

    shifter by:

    ointerm

    iddd VYEi

    , (11)

    where Vinterm

    is the value of bilateral imports of intermediates. If we summed across all partners,

    this measure would include part of the bilateral flow to be explained (namely intermediates from

    nation-o to nation-d). To avoid putting the trade flow to be explained on both sides of the

    equation, we build the measure for each pair in a way that excludes the pair‟s bilateral trade.

    For the economic mass variable size pertinent to the origin nation, we are trying to capture gross

    output that must be sold. The proposed measure is a straightforward application of the theory; it

    uses the origin nation‟s value added in manufacturing and its purchases of intermediate inputs

    from all sources except from itself (due to a lack of data).

    0i

    ,i

    nterm

    oi

    manuf

    oo VAVC (12)

    Note that our specification of the gravity equation uses the exports from nation-o to nation-d, so

    the second term in this does not include the bilateral flow to be explained. The second term

    involves nation-o‟s imports from all nations, not its exports to nations.

    4.2. Empirical results

    To test whether these proposed proxies work better than GDP, we run regressions like those

    reported in Table 4 but with the new proxies for economic mass replacing the standard proxy

    (i.e. GDP). The results are shown in Table 6.

    The results in Table 6 – compared with those in Table 4 – suggest that our proxies work better

    than GDP. The key piece of evidence can be seen in column (1). This includes the ratio of

    intermediates in total bilateral trade both on its own and interacted with the mass variable. The

    lack of significant of the ratio in either role suggests that our new proxy is doing a better job than

    GDP did in picking up demand and supply of intermediates.

    Interestingly, the column (2) regression, which allows an interaction between distances on the

    ratio of intermediates, suggests that the distance coefficient may also be mis-specified. When the

    ratio is interacted with distance, the distance estimate falls somewhat on average but especially

    for trade flows where parts and components are especially important (i.e. the ratio is high).

    This suggests that distance is more important, not less, for bilateral trade flows dominated by

    intermediates. The finding may reflect the well-known fact that most production fragmentation

    arrangements are regional, not global (components trade is more regionalised that overall trade).

    This result, however intriguing, does not really stand up to minor changes in the specification. In

    regression (4), which includes the ratio‟s interaction with all variables, the distance result fades;

    indeed only the common language effect seems to be magnified for trade flows marked by

    particularly high ratios of intermediates.

    Table 6: New mass proxies with share of intermediate, all nations, 2000-2007.

    VARIABLES (1) (2) (3) (4)

  • 18

    Mdinterm

    /Md 1.180 2.644** 2.044** 1.907*

    (1.020) (1.142) (0.988) (1.143)

    Ln (EdCo/ΩoPd) 0.898*** 0.889*** 0.945*** 0.932***

    (0.012) (0.0116) (0.012) (0.012)

    * Mdinterm

    /Md -0.0322 -0.0132 -0.0289 -0.0247

    (0.020) (0.020) (0.020) (0.020)

    ln (Distance) -1.080*** -0.929*** -0.908*** -0.838***

    (0.018) (0.038) (0.019) (0.038)

    * Mdinterm

    /Md

    -0.279***

    -0.131*

    (0.065)

    (0.067)

    Contigod

    1.441*** 1.211***

    (0.092) (0.224)

    *Mdinterm

    /Md

    0.356

    (0.354)

    Common language

    1.251*** 1.047***

    (0.047) (0.088)

    * Mdinterm

    /Md

    0.385***

    (0.143)

    Constant -20.05*** -20.87*** -24.17*** -24.08***

    (0.623) (0.687) (0.610) (0.685)

    Observations 87258 87258 87258 87258

    R-squared 0.607 0.607 0.631 0.631

    Note: Robust standard errors are reported in parenthesis: *** p

  • 19

    Table 7: New mass proxies with intermediate deciles, all nations, 2000-2007.

    Ln (EdCo/ΩoPd) ln (Distance) Constant

    Base effect 0.877*** -1.051*** -19.29***

    (0.022) (0.018) (1.074)

    Base effect * d2 0.0402

    (0.024)

    Base effect * d3 0.0365***

    (0.025)

    Base effect * d4 0.0294

    (0.024)

    Base effect * d5 -0.0256

    (0.024)

    Base effect * d6 -0.0531**

    (0.025)

    Base effect * d7 -0.0390

    (0.025)

    Base effect * d8 -0.0306

    (0.026)

    Base effect * d9 -0.0652**

    (0.028)

    Base effect * d10 0.0102 (0.027)

    Observations 87251 R-squared 0.609

    Notes: See notes to Table 5.

    5. WHY DO INCORRECTLY SPECIFIED MASS VARIABLES MATTER?

    A large number of gravity studies focus on variable that vary across country pairs – say free

    trade agreements, cultural ties, or immigrant networks. The most recent of these studies employ

    estimators that control for the mass variables with fixed effects.8 Such studies do not suffer from

    mass-variable mis-specification and so are unaffected by our critique.

    There are however as mentioned in the introduction, a number of recent studies – especially

    concerning the „distance puzzle‟ that do proxy for the production and demand variables with

    GDP. It is these studies that our work speaks to.9

    8 These econometric techniques were introduced by Harrigan (1996), Head and Mayer (2000),

    and Combes, Lafourcade and Mayer (2005), Anderson and Van Wincoop (2003), and Feenstra

    (2004).

    9 Rauch (1999), Brun et al (2005), Berthelon and Freund (2008), Jacks et al (2008), and

    Anderson and Van Wincoop (2003).

  • 20

    However, since most of these studies are concerned with a broad set of nations and commodities,

    the mis-specification of the mass variable probably has a minor impact on the results – as the

    findings of Bergstrand and Egger (2010) showed and we confirmed with our Table 1 results.

    More worrying, however, is the use by authors that focus on trade in parts and components.10

    These papers use the consumer-good version of the gravity model and thus mis-specify the mass

    variable.

    Once the equation is mis-specified – in particular the standard economic mass proxies are not

    correctly reflecting the supply and demand constraints – we are in the realm of omitted variable

    biases. The first task is to explore the nature of the biases that would arise from this mis-

    specification. To simplify, assume away GDPs and distance and focus on a pair-wise policy

    variable, say, nation-d‟s tariffs on imports from nation-o; we denote this as Tod. The estimated

    gravity equation will have the following structure:

    odtodtodt TV lnaconstantln 5 (13)

    where the error is assumed to be iid.

    Because intermediates supply is measured by total costs rather than GDP, and the supply of

    intermediates that must be sold depends upon gross output rather than value added. This means

    that the true model includes an additional term. That is:

    odtodtodtodt ZTV lnalnaaln 650 (14)

    where Zodt is the difference between the GDP-based mass variables and the true mass variables

    as specified in (7). We can write Zodt as a function of Todt in an auxiliary regression:

    odtodtodt uTbZ lnbln 10

    (15)

    where u is assumed to be iid. Using this notation for the coefficients of the auxiliary regression,

    we can see that in estimating (13), we are actually estimating:

    )(ln)aa()a(ln 616560 odtodtodtoodt uaTbabV

    (16)

    What this tells us is that the coefficient on the policy variable of interest will almost surely be

    biased. The point is that the only way it is not biased is if there is no correlation between the mis-

    specification of the economic mass variables and the policy variable.

    What sort of correlation should we expect? Recall that the mis-measurement of the economic

    mass variable all goes back to the importance of trade in intermediate goods. Since almost all

    bilateral variables of interest are things that affect bilateral trade flows, it seems extremely likely

    that the variable of interest will also affect the flow of intermediates. As long as it does, then we

    know that the mis-specification of the mass variable will also lead to a bias in the pair-wise

    variables.11

    10 Athukorala and Yamashita (2006), Kimura et al (2007), Yokota and Kazuhiko (2008), and

    Ando and Kimura (2009).

    11 As noted above, the modern techniques for controlling for mass with time-varying country-

    specific dummies eliminates such biases since they correctly control for the role of intermediates.

  • 21

    For example, let us suppose that tariffs discourage trade overall, but they especially discourage

    intermediates trade (for the usual effective rate of protection reasons, i.e. the tariff is paid on the

    gross trade value but its incidence falls on the value added only). In this case, we should expect

    low tariffs to encourage two things, an overall increase in trade and an increase in the ratio of

    intermediates. In this case, the bias in the mis-specified gravity equation is likely to be negative,

    since the policy variable is negatively correlated with the omitted variable. Furthermore, the mis-

    specification also affects the standard errors, which would result in a biased inference

    (Wooldridge, 2003, ch.4).

    6. CONCLUDING REMARKS

    In this paper we present empirical evidence that the standard gravity model performs poorly by

    some measures when it is applied to bilateral flows where parts and components trade is

    important. The paper also provides a simple theoretical foundation for a modified gravity

    equation that is suited to explaining trade where international supply chains are important.

    Finally we suggest ways in which the theoretical model can be implemented empirically.

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    APPENDIX

    Classification for intermediate and final goods

    BEC categories

    Intermediate goods: 111 - Primary food and beverages, mainly for industry

    121 - Processed food and beverages, mainly for industry

    21 - Primary industrial supplies not elsewhere specified

    22 - Processed industrial supplies not elsewhere specified

    32 - Processed fuels and lubricants

    42 - Parts and accessories of capital goods (except transport

    equipment)

    53 - Parts and accessories of transport equipment

    Consumption goods: 112 - Primary food and beverages, mainly for household consumption

    122 – Processed food and beverages, mainly for industry

    51 - Passenger motor cars

    6 - Consumer goods not elsewhere specified

    Other: 31 - Primary fuels and lubricants

    41 - Capital goods, excluding parts and components

    51 - Other transport equipment

    7 - Other

    Source: Comtrade‟s Broad Economic Categories; for details see

    http://unstats.un.org/unsd/tradekb/Knowledgebase/Intermediate-Goods-in-Trade-Statistics

    http://unstats.un.org/unsd/tradekb/Knowledgebase/Intermediate-Goods-in-Trade-Statistics